An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 5, 2016, Pages 3137-3147

  Lei, Yaguo ^a   Jia, Feng ^a   Lin, Jing ^a   Xing, Saibo ^a   Ding, Steven X ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b UNIVERSITY OF DUISBURG ESSEN   (Germany)

Author keywords

Intelligent fault diagnosis; mechanical big data; softmax regression; sparse filtering; unsupervised feature learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; ELECTROMECHANICAL FILTERS; FAILURE ANALYSIS; NETWORK LAYERS; VIBRATIONS (MECHANICAL);

ARTIFICIAL INTELLIGENCE TECHNIQUES; INTELLIGENT DIAGNOSIS; INTELLIGENT DIAGNOSIS METHODS; INTELLIGENT FAULT DIAGNOSIS; LEARNING METHODS; MECHANICAL VIBRATION SIGNALS; SOFTMAX REGRESSIONS; UNSUPERVISED FEATURE LEARNING;

FAULT DETECTION;

EID: 84963934455     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2016.2519325     Document Type: Article

References (51)

1. S. J. Qin, "Process data analytics in the era of big data," AIChE J., vol. 60, no. 9, pp. 3092-3100, Jul. 2014.
2. F. Frankel and R. Reid, "Big data: Distilling meaning from data," Nature, vol. 455, no. 7209, pp. 30-30, Sep. 2008.
3. X.-W. Chen and X. Lin, "Big data deep learning: Challenges and perspectives," IEEE Access, vol. 2, pp. 514-525, May 2014.
4. X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, "Data mining with big data," IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97-107, Jan. 2014.
5. S. Yin and O. Kaynak, "Big data for modern industry: Challenges and trends," Proc. IEEE, vol. 103, no. 2, pp. 143-146, Feb. 2015.
6. W. Qiao and D. Lu, "A survey on wind turbine condition monitoring and fault diagnosis," IEEE Trans. Ind. Electron., vol. 62, no. 10, pp. 6536-6545, Oct. 2015.
7. Y. Lei, J. Lin, M. J. Zuo, and Z. He, "Condition monitoring and fault diagnosis of planetary gearboxes: A review," Measurement, vol. 48, pp. 292-305, Feb. 2014.
8. J. Yu, "A nonlinear probabilistic method and contribution analysis for machine condition monitoring," Mech. Syst. Signal Process., vol. 37, no. 1, pp. 293-314, May 2013.
9. K. Worden, W. J. Staszewski, and J. J. Hensman, "Natural computing for mechanical systems research: A tutorial overview," Mech. Syst. Signal Process., vol. 25, no. 1, pp. 4-111, Jan. 2011.
10. Y. Shatnawi and M. Al-khassaweneh, "Fault diagnosis in internal combustion engines using extension neural network," IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1434-1443, Mar. 2014.
11. M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, "Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks," IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3398-3407, Aug. 2013.
12. D. You, X. Gao, and S. Katayama, "WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM," IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 628-638, Jan. 2015.
13. Y. Lei, Z. He, Y. Zi, and X. Chen, "New clustering algorithm-based fault diagnosis using compensation distance evaluation technique," Mech. Syst. Signal Process., vol. 22, no. 2, pp. 419-435, Feb. 2008.
14. M. Kang, J. Kim, J.-M. Kim, A. C. Tan, E. Y. Kim, and B.-K. Choi, "Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis," IEEE Trans. Power Electron., vol. 30, no. 5, pp. 2786-2797, May 2015.
15. S. Yin, S. X. Ding, X. Xie, and H. Luo, "A review on basic datadriven approaches for industrial process monitoring," IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418-6428, Nov. 2014.
16. Y. Lei, Z. He, Y. Zi, and Q. Hu, "Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs," Mech. Syst. Signal Process., vol. 21, no. 5, pp. 2280-2294, Jul. 2007.
17. J. Yu, "Local and nonlocal preserving projection for bearing defect classification and performance assessment," IEEE Trans. Ind. Electron., vol. 59, no. 5, pp. 2363-2376, May 2012.
18. M. Amar, I. Gondal, and C. Wilson, "Vibration spectrum imaging: A novel bearing fault classification approach," IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 494-502, Jan. 2015.
19. J. Liu, W. Wang, and F. Golnaraghi, "An enhanced diagnostic scheme for bearing condition monitoring," IEEE Trans. Instrum. Meas., vol. 59, no. 2, pp. 309-321, Feb. 2010.
20. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
21. A. Coates, A. Y. Ng, and H. Lee, "An analysis of single-layer networks in unsupervised feature learning," in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS), 2011, pp. 215-223.
22. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May 2015.
23. K. Yu, Y. Lin, and J. Lafferty, "Learning image representations from the pixel level via hierarchical sparse coding," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2011, pp. 1713-1720.
24. M. A. Ranzato, F. J. Huang, Y.-L. Boureau, and Y. LeCun, "Unsupervised learning of invariant feature hierarchies with applications to object recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1-8.
25. G. Hinton et al., "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82-97, Nov. 2012.
26. P. Baldi, P. Sadowski, and D. Whiteson, "Searching for exotic particles in high-energy physics with deep learning," Nat. Commun., vol. 5, no. 4308, pp. 1-9, Jul. 2014.
27. G. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.
28. G. Hinton, "A practical guide to training restricted Boltzmann machines," in Neural Networks: Tricks of the Trade, K.-R. Muller, G. Montavon, and G. B. Orr, Eds. New York, NY, USA: Springer, 2013.
29. J. Ngiam, Z. Chen, S. A. Bhaskar, P. W. Koh, and A. Y. Ng, "Sparse filtering," in Proc. Neural Inf. Process. Syst., 2011, pp. 1125-1133.
30. K. B. Raja, R. Raghavendra, V. K. Vemuri, and C. Busch, "Smartphone based visible iris recognition using deep sparse filtering," Pattern Recognit. Lett., vol. 57, pp. 33-42, May 2015.
31. D. J. Field, "What is the goal of sensory coding?" Neural Comput., vol. 6, no. 4, pp. 559-601, Jul. 1994.
32. B. Willmore and D. J. Tolhurst, "Characterizing the sparseness of neural codes," Netw.: Comput. Neural Syst., vol. 12, no. 3, pp. 255-270, 2001.
33. R. G. Baraniuk, "Compressive sensing," IEEE Signal Process. Mag., vol. 24, no. 4, pp. 118-120, Jul. 2007.
34. J. Behley, V. Steinhage, and A. Cremers, "Laser-based segment classification using a mixture of bag-of-words," in Proc. IEEE Int. Conf. Intell. Robots Syst., 2013, pp. 4195-4200.
35. J. Han, D. Zhang, S.Wen, L. Guo, T. Liu, and X. Li, "Two-stage learning to predict human eye fixations via SDAEs," IEEE Trans. Cybern., vol. 46, no. 2, pp. 487-498, Feb. 2016.
36. C. Bielza, V. Robles, and P. Larrañaga, "Regularized logistic regression without a penalty term: An application to cancer classification with microarray data," Expert Syst. Appl., vol. 38, no. 5, pp. 5110-5118, May 2011.
37. A. Hyvärinen and E. Oja, "Independent component analysis: Algorithms and applications," Neural Netw., vol. 13, no. 4, pp. 411-430, Jun. 2000.
38. N. Jaitly and G. Hinton, "Learning a better representation of speech soundwaves using restricted boltzmann machines," in Proc. Int. Conf. Acoust. Speech Signal Process., 2011, pp. 5884-5887.
39. X. Lou and K. A. Loparo, "Bearing fault diagnosis based on wavelet transform and fuzzy inference," Mech. Syst. Signal Process., vol. 18, no. 5, pp. 1077-1095, Sep. 2004.
40. M. Sokolova, N. Japkowicz, and S. Szpakowicz, "Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation," in Proc. Adv. Artif. Intell. (AI'06), 2006, pp. 1015-1021.
41. W. Du, J. Tao, Y. Li, and C. Liu, "Wavelet leaders multifractal features based fault diagnosis of rotating mechanism," Mech. Syst. Signal Process., vol. 43, no. 1, pp. 57-75, Feb. 2014.
42. X. Jin, M. Zhao, T. W. Chow, and M. Pecht, "Motor bearing fault diagnosis using trace ratio linear discriminant analysis," IEEE Trans. Ind. Electron., vol. 61, no. 5, pp. 2441-2451, May 2014.
43. W. Li, S. Zhang, and G. He, "Semisupervised distance-preserving selforganizing map for machine-defect detection and classification," IEEE Trans. Instrum. Meas., vol. 62, no. 5, pp. 869-879, May 2013.
44. X. Zhang, Y. Liang, and J. Zhou, "A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM," Measurement, vol. 69, pp. 164-179, Jun. 2015.
45. Z. R. Struzik and A. Siebes, "The Haar wavelet transform in the time series similarity paradigm," in Proc. 3rd Eur. Conf. Principles Data Min. Knowl. Discov., 1999, pp. 12-22.
46. E. C. Smith and M. S. Lewicki, "Efficient auditory coding," Nature, vol. 439, no. 7079, pp. 978-982, Feb. 2006.
47. G. Masson, C. Busettini, and F. Miles, "Vergence eye movements in response to binocular disparity without depth perception," Nature, vol. 389, no. 6648, pp. 283-286, Sep. 1997.
48. J. Qiu, G. Feng, and H. Gao, "Fuzzy-model-based piecewise H-infinity static output feedback controller design for networked nonlinear systems," IEEE Trans. Fuzzy Syst., vol. 18, no. 5, pp. 919-934, Oct. 2010.
49. T. Wang, H. Gao, and J. Qiu, "A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control," IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 2, pp. 416-425, Feb. 2016.
50. H. Li, S. Yin, Y. Pan, and H. K. Lam, "Model reduction for interval type-2 Takagi-Sugeno fuzzy systems," Automatica, vol. 61, pp. 308-314, Nov. 2015.
51. J. Qiu, S. X. Ding, H. Gao, and S. Yin, "Fuzzy-model-based reliable static output feedback H-infinity control of nonlinear hyperbolic PDE systems," IEEE Trans. Fuzzy Syst., to be published.